Detailed exploration of key mathematical and statistical concepts for Machine Learning.
Logical flow from basic to advanced topics for seamless knowledge building.
Engaging materials designed to enhance learning and retention.

Description

Beginning with basic concepts like central tendency, dispersion, and types of distribution, this course will help you build a robust understanding of data analysis. It progresses to more advanced topics, including hypothesis testing, outliers, and the intricacies of dependent versus independent variables, ensuring you grasp the statistical tools necessary for data-driven decision-making. Moving ahead, you'll explore the mathematical frameworks crucial for machine learning algorithms. Learn about the significance of percentiles, the distinction between population and sample, and the vital role of precision versus accuracy in data science. Chapters on linear algebra and regression will enhance your ability to implement and interpret complex models, while practical lessons on measuring algorithm accuracy and understanding key machine learning concepts will round out your expertise. The course culminates with an in-depth look at specific machine learning techniques such as decision trees, k-nearest neighbors (kNN), and gradient descent. Each chapter builds on the last, guiding you through a logical progression of knowledge and skills. By the end, you will have not only mastered the theoretical aspects but also gained practical insights into applying these techniques in real-world scenarios.

What you will learn

Master the fundamentals of central tendency and dispersion. Understand the different types of data distributions. Differentiate between precision and accuracy in data analysis. Conduct hypothesis testing and identify outliers. Apply linear algebra and regression techniques in machine learning. Implement decision trees, kNN, & gradient descent algorithms.

What do you get with eBook?